AWaRe: Modeling Awareness Across Disciplines

• AWaRe is a multidisciplinary framework that formalizes awareness across epistemic logic, AI introspection, cybersecurity, and astrophysical signal analysis.
• It employs dynamic operators, fuzzy risk assessments, and attention-based deep learning to mitigate logical omniscience, adapt to situational changes, and quantify uncertainty.
• Applications include enhancing agent reasoning, evaluating large language models, securing enterprise networks, and improving gravitational wave signal reconstruction.

AWaRe is an acronym that designates distinct technical systems and frameworks across multiple research domains, each centered on the concept of "awareness"—ranging from epistemic logic in agent reasoning, to benchmarking and enhancing situational or self-awareness in artificial intelligence, to uncertainty-aware signal reconstruction in astrophysics. The following provides an authoritative survey of the principal AWaRe concepts and models as reflected in recent literature.

1. Model-Theoretic Awareness in Epistemic Logic

In the domain of formal reasoning about knowledge and awareness, the "AWaRe" framework denotes an approach for representing and reasoning about agents’ limited awareness and its ramifications for epistemic logic (Kubono et al., 2023). Here, awareness is not only the set of atomic propositions an agent can refer to, but also operationalizes the indistinguishability between possible worlds based on what the agent is aware of.

A core construct is the operator $A^i_j \varphi$, which is satisfied at a world $w$ if all atomic propositions in $\varphi$ reside in agent $j$’s awareness set as perceived by agent $i$. Formally:

$$M, w \vDash A^i_j \varphi \iff \mathrm{At}(\varphi) \subseteq \mathcal{A}^i_j,$$

with $\mathcal{A}^i_j$ the awareness set and $\mathrm{At}(\varphi)$ the propositional atoms in $\varphi$. This model is extended via the indistinguishability relation $\equiv^i_j$ on worlds, defined so $(w, v) \in \equiv^i_j$ if $w$ and $v$ agree on all $p \in \mathcal{A}^i_j$. This generalizes Kripke semantics, and induces a partitioning of the model space: an agent cannot distinguish between worlds differing only on facts outside its awareness.

A significant outcome is the circumvention of "logical omniscience": agents only reason with propositions they are aware of, and thus cannot derive arbitrary logical consequences from implicit knowledge. The framework is further equipped with dynamic operators $[+\varphi]^i_j$ and $[-\varphi]^i_j$, modeling epistemic actions that update the agent’s awareness sets. Completeness is established through a canonical model construction adapted for the awareness and indistinguishability modalities.

2. Benchmarking and Taxonomy of Awareness in LLMs

The term "AWaRe" also demarcates benchmarks and frameworks for evaluating and improving awareness in LLMs (Li et al., 31 Jan 2024). In this context, awareness encompasses both introspective recognition—such as knowledge of one’s own capabilities and duties ("capability" and "mission" awareness)—and social faculties, including emotion, cultural, and perspective awareness.

The AwareBench and its associated dataset AwareEval operationalize this taxonomy with a suite of binary, multiple-choice, and open-ended tasks. Capability awareness, for instance, tests a model’s recognition of its own operational limits; mission awareness probes understanding of the primacy of human interests; emotion, culture, and perspective awareness measure proficiency in social reasoning, cultural identification, and theory of mind tasks, respectively.

Experimental evaluation across 13 LLMs reveals substantial deficits in introspective awareness, with leading proprietary systems (e.g., GPT-4) only beginning to approach 80% accuracy on relevant tasks, and open models often below 20%. Social awareness tasks elicit higher scores, but overall there remain significant gaps correlated with model scale and architecture. These findings raise critical implications for AI safety and alignment: LLMs lacking self- and mission-awareness are at heightened risk of producing erroneous or misaligned outputs.

3. Situational Awareness and Risk-Adaptive Access Control in Enterprise Security

In risk-adaptive cybersecurity, awareness is formalized at the enterprise level by quantifying situational risk and propagating this into access control decisions (Lee et al., 2017). The principal construct is Security Situational Awareness (SSA), measured via mission dependency graphs which trickle asset criticality from organizational objectives down to IT infrastructure. This enables quantification of the operational impact of breaches.

This situational model is integrated into Risk-Adaptable Access Control (RAdAC), which reevaluates access policies dynamically, blending operational necessity with risk assessed in real time. These decisions are implemented in the FURZE ("Fuzzy Risk Framework for Zero Trust Networking") architecture, which augments classical UCON models with fuzzy risk evaluation (using Fuzzy Cognitive Maps and FCL) to flexibly express and reason about "high threat", "low device trust", etc. The system’s context handler ensures decision continuity—access rights dynamically adapt as session parameters and the environment shift. This facilitates robust policy management in zero trust network environments.

4. Attention-Boosted Waveform Reconstruction in Gravitational Wave Astrophysics

The AWaRe model (Attention-boosted Waveform Reconstruction) in gravitational wave data analysis embodies a statistical deep learning framework for time-series signal reconstruction under uncertainty (Chatterjee et al., 10 Jun 2024). The architecture comprises a convolutional encoder for temporal feature extraction, augmented by a multi-headed attention mechanism to capture relevant modulations (e.g., higher harmonics, eccentric effects), and a decoder realized with Long Short-Term Memory (LSTM) layers for sequential signal prediction.

Critically, AWaRe models each output time sample as a Gaussian, emitting both a mean prediction and variance:

$$p(\hat{h}_i \mid x) = \mathcal{N}(\hat{h}_i \mid \mu_i, \sigma_i^2),$$

thereby providing uncertainty estimates aligned with established pipelines such as BayesWave and coherent WaveBurst. The network is optimized using a negative log-likelihood loss that encompasses both mean squared error and variance regularization:

$$\mathcal{L}_i = \frac{1}{2}\log(2\pi\sigma_i^2) + \frac{(h_i - \mu_i)^2}{2\sigma_i^2}$$

This approach enables AWaRe to extrapolate to unseen signal morphologies (including higher-mass binary mergers and eccentric signals) and to deliver low-latency, confidence-qualified waveform reconstructions.

5. Implications, Applications, and Comparative Perspectives

Across disparate domains, AWaRe frameworks demonstrate several convergent themes:

• Avoidance of Omniscience and Overfitting: By explicitly modeling (un)awareness, both agent logics and LLMs achieve more humanlike, bounded reasoning and avoid unwarranted generalization.
• Dynamic Policy Adaptation: Situational awareness models and risk-adaptive frameworks enable continuous reassessment and adaptation by embedding awareness into ongoing access or control decisions.
• Uncertainty Quantification: In signal reconstruction, explicit modeling of per-sample uncertainty enables principled quantification of output reliability rather than point estimates.

Significant research opportunities remain in extending awareness logics to broader classes of epistemic actions, refining fuzzy inference in dynamic policy frameworks, advancing introspective capabilities in LLMs to support alignment and safety, and scaling uncertainty-aware deep learning models to more complex, real-time astrophysical environments.

6. Summary Table: Principal AWaRe Models

Domain	Core Concept	Salient Innovations
Epistemic Logic (Kubono et al., 2023)	Agent (un)awareness as partition of possible worlds; avoidance of logical omniscience	Awareness operators, dynamic epistemic actions, canonical completeness proof
LLM Benchmarking (Li et al., 31 Jan 2024)	Taxonomy and evaluation of model awareness	AwareBench/AwareEval, introspective vs. social awareness, AI alignment
Cybersecurity (Lee et al., 2017)	Situational awareness for risk-adaptive access	FURZE, fuzzy control, mission dependency graphs, fuzzy cognitive maps
Astrophysics (Chatterjee et al., 10 Jun 2024)	Uncertainty-aware deep signal reconstruction	Encoder-attention-decoder, per-sample uncertainty, low-latency, generalization

Each variant of AWaRe, though disciplinary-specific, employs rigorous awareness modeling—whether of agent cognition, self-knowledge in AI, system risk in networks, or uncertainty in time-series reconstruction—with the purpose of improving performance, robustness, and alignment with underlying operational or epistemic desiderata.